COVID-19 Analysis

Read data

df <- read_excel("wuhan_blood_sample_data_Jan_Feb_2020.xlsx")

Clean dataset

patients_df <- df %>% group_by(`Admission time`, `Discharge time`, gender, age, outcome) %>%
summarise(PATIENT_ID = sum(PATIENT_ID, na.rm = TRUE), `Total records` = n()) 

patients_df <- patients_df %>%
    mutate(`Days in hospital` = ceiling(difftime(`Discharge time`, `Admission time`, units = "days")))

df <- full_join(patients_df %>% ungroup() %>% select(`Admission time`, PATIENT_ID), df %>% select(-PATIENT_ID), by="Admission time")

df$gender<-ifelse(df$gender==1, 'Male', 'Female') 
df <- df %>% mutate(gender = as.factor(gender))

patients_df$gender<-ifelse(patients_df$gender==1, 'Male', 'Female') 
patients_df <- patients_df %>% mutate(gender = as.factor(gender))

df$outcome<-ifelse(df$outcome==1, 'Death', 'Survival')
df <- df %>% mutate(outcome = as.factor(outcome))

patients_df$outcome<-ifelse(patients_df$outcome==1, 'Death', 'Survival')
patients_df <- patients_df %>% mutate(outcome = as.factor(outcome))

patients_df <- df %>% group_by(PATIENT_ID, outcome, `Admission time`, `Discharge time`, gender, age) %>% summarise(missing_test=is.na(RE_DATE)) %>%
  group_by(PATIENT_ID, outcome, `Admission time`, `Discharge time`, gender, age) %>%
  summarise(test_provided = ifelse(missing_test, 0, 1)) %>%
  group_by(PATIENT_ID, outcome, `Admission time`, `Discharge time`, gender, age) %>%
  summarise(`Total blood tests`=sum(test_provided)) %>%
  mutate(`Days in hospital` = ceiling(difftime(`Discharge time`, `Admission time`, units = "days")))

last_test_df <- df %>% 
  group_by(PATIENT_ID, outcome, gender, age) %>% 
  fill_(names(df)) %>% 
  fill_(names(df), "up") %>% 
  summarise_at(vars(`Hypersensitive cardiac troponinI`:creatinine), function(x) last(x,order_by = is.na(x)))


cleaned_df <- last_test_df %>% ungroup() %>% select(PATIENT_ID, outcome, age, gender, hemoglobin, `eosinophils(%)`, `Alkaline phosphatase`, albumin, `basophil(%)`, `Total bilirubin`, `Platelet count`, `monocytes(%)`, `neutrophils(%)`, `total protein`, `mean corpuscular volume`, hematocrit, `White blood cell count`, `mean corpuscular hemoglobin concentration`, Urea, `lymphocyte count`, `Red blood cell count`, `Eosinophil count`, `neutrophils count`, `Direct bilirubin`, `(%)lymphocyte`, `Total cholesterol`, `aspartate aminotransferase`, `Uric acid`, `HCO3-`, `Lactate dehydrogenase`, `monocytes count`, globulin, `γ-glutamyl transpeptidase`, `basophil count(#)`, `mean corpuscular hemoglobin`, `glutamic-pyruvic transaminase`, eGFR, creatinine) %>% filter_all(function(x) !is.na(x))

Patients summary

patients_summary <- patients_df %>% ungroup() %>% select(-c(age, PATIENT_ID))

tbl_summary(
    patients_summary,
    by = outcome,
    label = gender ~ "Gender",
) %>%
    add_n() %>%
    modify_header(label = "") %>%
    add_overall() %>%
    bold_labels()

	Overall, N = 375¹	N	Death, N = 174¹	Survival, N = 201¹
Gender		375
Female	151 (40%)		48 (28%)	103 (51%)
Male	224 (60%)		126 (72%)	98 (49%)
Total blood tests	16 (9, 21)	375	14 (7, 24)	16 (12, 20)
Days in hospital	10 (5, 16)	375	6 (3, 10)	14 (10, 18)
¹ Statistics presented: n (%); Median (IQR)

 ggplot(patients_df, aes(x=age,fill=gender)) + geom_histogram(binwidth = 1) + facet_grid(. ~ gender) + scale_x_continuous(name="Age", limits=c(min(df$age), max(df$age)), breaks = seq(0, 100, by=10)) + scale_y_continuous(name = "Number of patients", limits = c(0,10), breaks = seq(0,10, by=1)) +
    theme_minimal()

Patients grouped by outcome, age and gender

 ggplot(patients_df, aes(x=age,fill=outcome)) + geom_histogram(binwidth = 1) + facet_grid(outcome ~ gender) + scale_x_continuous(name="Age", limits=c(min(df$age), max(df$age)), breaks = seq(0, 100, by=10)) + scale_y_continuous(name = "Number of patients", limits = c(0,9), breaks = seq(0,9, by=1)) +
    theme_minimal()

Patients grouped by outcome and hospitalization duration

ggplot(patients_df, aes(x=`Days in hospital`, fill=outcome)) + geom_histogram(binwidth = 1) + facet_grid(. ~ outcome)  + ylab("Number of patients") +
    theme_minimal()

Patients grouped by outcome and total blood tests taken

ggplot(patients_df, aes(x=`Total blood tests`, fill=outcome)) + geom_histogram(binwidth = 1) + facet_grid(. ~ outcome)  + ylab("Number of patients") +
  scale_x_continuous(name="Total blood tests", limits=c(0, 60), breaks = seq(0, 60, by=10)) +
   scale_y_continuous(name = "Number of patients", limits = c(0,20), breaks = seq(0,20, by=2)) +
    theme_minimal()

Removed data

patients_df %>% filter(`Total blood tests`==0)

PATIENT_ID	outcome	Admission time	Discharge time	gender	age	Days in hospital
187	Survival	2020-02-17 18:56:09	2020-02-20 20:55:31	Male	44	4 days
189	Survival	2020-02-10 04:37:30	2020-02-10 13:54:23	Male	61	1 days
192	Survival	2020-02-16 17:14:30	2020-02-16 21:05:17	Male	34	1 days
197	Survival	2020-02-10 05:01:15	2020-02-11 15:40:41	Male	67	2 days
200	Survival	2020-02-16 04:41:21	2020-02-16 15:26:13	Male	25	1 days
201	Survival	2020-02-17 21:30:07	2020-02-20 13:05:11	Male	39	3 days
253	Death	2020-02-13 21:05:54	2020-02-14 11:00:05	Male	51	1 days
268	Death	2020-02-14 11:46:36	2020-02-15 10:15:28	Male	69	1 days
285	Death	2020-01-31 23:20:40	2020-02-01 03:16:34	Male	63	1 days
289	Death	2020-02-01 02:12:05	2020-02-01 10:54:57	Male	63	1 days
311	Death	2020-02-11 23:45:15	2020-02-15 09:02:41	Female	77	4 days
347	Death	2020-02-11 22:25:20	2020-02-15 10:03:32	Female	80	4 days
354	Death	2020-02-03 21:22:41	2020-02-04 01:03:11	Male	57	1 days
359	Death	2020-02-11 01:42:48	2020-02-14 09:38:13	Male	65	4 days

Biomarkers

no_dates_df <- df %>% select(-c('Admission time', 'Discharge time', 'RE_DATE', 'PATIENT_ID', 'age', 'gender' ))
tbl_summary(
    no_dates_df,
    by = outcome,
    missing = "no"
            ) %>%
    add_n() %>%
    modify_header(label = "**Biomarker**") %>%
  modify_spanning_header(c("stat_1", "stat_2") ~ "**Final patient outcome related to the test**") %>%
    bold_labels()

Biomarker	N	Final patient outcome related to the test
Biomarker	N	Death, N = 2,905¹	Survival, N = 3,215¹
Hypersensitive cardiac troponinI	507	70 (18, 631)	3 (2, 7)
hemoglobin	975	123 (110, 135)	127 (116, 138)
Serum chloride	975	104 (100, 111)	101 (99, 103)
Prothrombin time	662	16.3 (15.0, 18.2)	13.6 (13.1, 14.1)
procalcitonin	459	0.38 (0.14, 1.13)	0.04 (0.02, 0.06)
eosinophils(%)	957	0.00 (0.00, 0.10)	0.70 (0.00, 1.80)
Interleukin 2 receptor	268	1,180 (807, 1,603)	529 (400, 742)
Alkaline phosphatase	930	83 (64, 123)	60 (50, 75)
albumin	934	28 (24, 31)	36 (34, 39)
basophil(%)	957	0.10 (0.10, 0.20)	0.20 (0.10, 0.40)
Interleukin 10	267	11 (6, 17)	5 (5, 8)
Total bilirubin	930	14 (10, 25)	8 (6, 12)
Platelet count	957	112 (55, 174)	229 (176, 290)
monocytes(%)	958	3.0 (2.0, 4.7)	8.2 (6.3, 10.0)
antithrombin	330	80 (70, 92)	93 (86, 103)
Interleukin 8	268	30 (18, 61)	11 (7, 19)
indirect bilirubin	906	6.2 (4.2, 9.2)	4.9 (3.4, 7.1)
Red blood cell distribution width	923	13.20 (12.40, 14.40)	12.20 (11.80, 12.80)
neutrophils(%)	957	92 (88, 95)	66 (56, 76)
total protein	931	62 (57, 68)	68 (65, 72)
Quantification of Treponema pallidum antibodies	279	0.06 (0.04, 0.07)	0.05 (0.04, 0.07)
Prothrombin activity	659	66 (56, 78)	94 (88, 103)
HBsAg	279	0.01 (0.00, 0.02)	0.00 (0.00, 0.01)
mean corpuscular volume	957	91.3 (87.1, 96.4)	89.8 (86.8, 91.9)
hematocrit	957	35.9 (32.5, 39.8)	37.1 (34.3, 39.9)
White blood cell count	1,127	12 (8, 17)	6 (4, 8)
Tumor necrosis factorα	268	11 (8, 17)	8 (6, 10)
mean corpuscular hemoglobin concentration	957	342 (331, 350)	343 (335, 350)
fibrinogen	566	3.92 (2.44, 5.63)	4.40 (3.56, 5.34)
Interleukin 1β	268	5.0 (5.0, 5.0)	5.0 (5.0, 5.0)
Urea	936	11 (7, 17)	4 (3, 5)
lymphocyte count	957	0.46 (0.31, 0.69)	1.25 (0.87, 1.62)
PH value	384	6.50 (6.00, 7.41)	6.50 (6.00, 7.00)
Red blood cell count	1,127	4.0 (3.6, 4.6)	4.2 (3.8, 4.7)
Eosinophil count	957	0.00 (0.00, 0.01)	0.03 (0.00, 0.09)
Corrected calcium	914	2.35 (2.27, 2.44)	2.37 (2.27, 2.44)
Serum potassium	980	4.60 (4.04, 5.27)	4.28 (3.92, 4.62)
glucose	775	9.1 (6.9, 13.3)	5.7 (5.0, 7.6)
neutrophils count	957	10.8 (7.0, 15.2)	3.5 (2.4, 5.2)
Direct bilirubin	930	8 (5, 14)	4 (2, 5)
Mean platelet volume	862	11.30 (10.70, 12.20)	10.40 (9.90, 11.00)
ferritin	283	1,636 (928, 2,517)	504 (235, 834)
RBC distribution width SD	923	43.7 (39.9, 48.5)	39.5 (37.6, 41.4)
Thrombin time	566	17.30 (15.80, 19.75)	16.40 (15.60, 17.30)
(%)lymphocyte	958	4 (2, 7)	24 (16, 33)
HCV antibody quantification	279	0.07 (0.04, 0.11)	0.06 (0.04, 0.08)
D-D dimer	630	19 (3, 21)	1 (0, 1)
Total cholesterol	931	3.32 (2.72, 3.88)	3.93 (3.39, 4.48)
aspartate aminotransferase	935	38 (25, 59)	21 (17, 29)
Uric acid	934	245 (166, 374)	240 (193, 304)
HCO3-	934	21.8 (18.8, 24.7)	24.7 (22.8, 26.7)
calcium	979	2.00 (1.90, 2.08)	2.17 (2.10, 2.25)
Amino-terminal brain natriuretic peptide precursor(NT-proBNP)	475	1,467 (516, 4,578)	64 (23, 166)
Lactate dehydrogenase	934	593 (431, 840)	220 (189, 278)
platelet large cell ratio	862	35 (30, 42)	28 (23, 33)
Interleukin 6	272	66 (30, 142)	8 (2, 21)
Fibrin degradation products	330	114 (18, 150)	4 (4, 4)
monocytes count	957	0.36 (0.20, 0.58)	0.43 (0.32, 0.58)
PLT distribution width	862	13.60 (12.10, 15.93)	11.70 (10.70, 13.00)
globulin	930	34.1 (30.2, 38.2)	31.8 (29.5, 35.2)
γ-glutamyl transpeptidase	930	42 (27, 79)	29 (19, 46)
International standard ratio	659	1.31 (1.17, 1.48)	1.04 (0.99, 1.09)
basophil count(#)	957	0.010 (0.010, 0.030)	0.010 (0.010, 0.020)
2019-nCoV nucleic acid detection	501
-1		57 (100%)	444 (100%)
mean corpuscular hemoglobin	957	31.20 (29.90, 32.70)	30.70 (29.60, 31.90)
Activation of partial thromboplastin time	568	40 (36, 45)	39 (35, 43)
High sensitivity C-reactive protein	737	114 (65, 191)	7 (2, 35)
HIV antibody quantification	278	0.08 (0.07, 0.11)	0.09 (0.08, 0.11)
serum sodium	975	142 (138, 148)	140 (138, 141)
thrombocytocrit	862	0.15 (0.10, 0.21)	0.24 (0.19, 0.30)
ESR	383	36 (16, 59)	26 (13, 40)
glutamic-pyruvic transaminase	931	26 (18, 44)	21 (15, 36)
eGFR	936	72 (43, 91)	100 (85, 114)
creatinine	936	88 (68, 130)	64 (54, 83)
¹ Statistics presented: Median (IQR); n (%)

Finding correlation

Finding correlation between outcome and other variables. Ignoring Admission time, PATIENT_ID, RE_DATE and Discharge time, and dropping outcome, which correlation with itself is equal to 1.0.

cor_df <-df

cor_df$outcome<-ifelse(cor_df$outcome=='Death', 1, 0)
cor_df$gender<-ifelse(cor_df$gender=='Male', 1, 0)

cor_df <- cor_df %>% rename(isMale=gender)
cor_df <- cor_df %>% rename(Death=outcome)

cor_df <- cor_df %>% select (-c(`Admission time`, PATIENT_ID, RE_DATE, `Discharge time` ))

corrMatrix <- cor(cor_df[sapply(cor_df, is.numeric)], use='pairwise.complete.obs')

correlation_df <- as.data.frame(corrMatrix)

correlation_df %>% rownames_to_column('variable') %>% filter(variable != 'Death') %>% select(variable, Death) %>% mutate(Death = abs(Death)) %>%
  arrange(desc(Death)) %>%
  rename(`Outcome correlation` = Death) %>%
  head(10)

variable	Outcome correlation
neutrophils(%)	0.7258383
(%)lymphocyte	0.7230203
albumin	0.6892432
Prothrombin activity	0.6562405
High sensitivity C-reactive protein	0.6524696
D-D dimer	0.6368244
Lactate dehydrogenase	0.6230357
neutrophils count	0.6071749
Fibrin degradation products	0.6044110
age	0.5860651

Use of correlation

In following section, we will use 3 biomarkers that are most correlated to outcome and visualize theirs mean values among each patient at 3D graph. So we take only these patients who had all of these 3 biomarkers tested at least once. If somebody was tested more than once, then mean value of all tests for each biomarker is calculated.

#TODO: Take for correlation cleaned_df and check if it's consistent with predictive model

new_df  <- df

new_df <- new_df %>% select(PATIENT_ID, `neutrophils(%)`, `(%)lymphocyte`, albumin, outcome) %>%
    group_by(PATIENT_ID, outcome) %>%
    summarise(Neutrophils_mean = mean(`neutrophils(%)`, na.rm = TRUE), Lymphocyte_mean = mean(`(%)lymphocyte`, na.rm = TRUE), albumin_mean = mean(`albumin`, na.rm = TRUE))

new_df <- new_df %>% filter(!is.nan(Neutrophils_mean) & !is.nan(Lymphocyte_mean) & !is.nan(albumin_mean))


mycolors <- c('royalblue1', 'darkcyan')
new_df$color <- mycolors[ as.numeric(new_df$outcome) ]

par(mar=c(0,0,0,0))
plot3d( 
    x=new_df$Neutrophils_mean, y=new_df$Lymphocyte_mean, z=new_df$albumin_mean, 
    col = new_df$color, 
    type = 's', 
    radius = 1,
    legend=TRUE,
    xlab="Neutrophils(%)", ylab="Lymphocyte(%)", zlab="Albumin")
legend3d("topright", legend = c('Death', 'Survival'), pch = 10, col = mycolors, cex=0.8, inset=c(0.02))

writeWebGL( filename="3d_correlation_mean.html" ,  width=600, height=600)

Data after cleaning and transformation:

tbl_summary(
    new_df %>% ungroup() %>%rename(`Neutrophils(%)` = Neutrophils_mean, `Lymphocyte(%)` = Lymphocyte_mean, `Albumin`= albumin_mean) %>% select(-PATIENT_ID, -color),
    by = outcome
) %>%
    add_n() %>%
    modify_header(label = "**Mean value of**") %>%
    add_overall() %>%
    bold_labels()

Mean value of	Overall, N = 354¹	N	Death, N = 162¹	Survival, N = 192¹
Neutrophils(%)	77 (63, 91)	354	91 (87, 94)	66 (56, 72)
Lymphocyte(%)	15 (5, 26)	354	5 (3, 8)	25 (18, 31)
Albumin	33.1 (29.1, 37.0)	354	28.9 (26.3, 31.2)	36.5 (33.9, 39.4)
¹ Statistics presented: Median (IQR)

htmltools::includeHTML("./3d_correlation_mean.html")

RGL model

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Drag mouse to rotate model. Use mouse wheel or middle button to zoom it.

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Common biomarkers’ change in time

biomarker_popularity <- df %>% group_by(PATIENT_ID, outcome) %>% summarise_at(vars(`Hypersensitive cardiac troponinI`:creatinine), function(x) sum(!is.na(x)))
biomarker_popularity$sum_of_single_test <- rowSums(biomarker_popularity %>% ungroup() %>% select(-PATIENT_ID, -outcome))

most_tested_death <- biomarker_popularity %>% select(sum_of_single_test, outcome, `neutrophils(%)`,  `(%)lymphocyte`, albumin) %>% filter(outcome=='Death') %>% arrange(desc(sum_of_single_test)) %>% head(10)
most_tested_survival <- biomarker_popularity %>% select(sum_of_single_test, outcome, `neutrophils(%)`,  `(%)lymphocyte`, albumin) %>% filter(outcome=='Survival') %>% arrange(desc(sum_of_single_test)) %>% head(10)

foo <- rbind(most_tested_death, most_tested_survival)

patients_tests <- merge(foo %>% ungroup() %>% select(PATIENT_ID), df %>% ungroup() %>% select(PATIENT_ID, RE_DATE,`neutrophils(%)`,  `(%)lymphocyte`, albumin, outcome),  by="PATIENT_ID") %>% filter(!is.na(`neutrophils(%)`) |  !is.na(`(%)lymphocyte`) | !is.na(albumin))

neutrophils_seq <- patients_tests %>% filter(!is.na(`neutrophils(%)`)) 

neutrophils_seq %>% mutate(PATIENT_ID=as.factor(PATIENT_ID)) %>%
  ggplot( aes(x=RE_DATE, y=`neutrophils(%)`, group=PATIENT_ID, color=PATIENT_ID)) +
    geom_line() +
    geom_point() +
  facet_grid(rows=vars(outcome)) +
    ggtitle("Neutrophils (%) during patient hospitalization") +
    theme_ipsum() +
    ylab("Neutrophils (%)") +
    transition_reveal(RE_DATE)

#anim_save("neutrophils_seq.gif")

lymphocyte_seq <- patients_tests %>% filter(!is.na(`(%)lymphocyte`)) 

lymphocyte_seq %>% mutate(PATIENT_ID=as.factor(PATIENT_ID)) %>%
  ggplot( aes(x=RE_DATE, y=`(%)lymphocyte`, group=PATIENT_ID, color=PATIENT_ID)) +
    geom_line() +
    geom_point() +
  facet_grid(rows=vars(outcome)) +
    ggtitle("Lymphocyte (%) during patient hospitalization") +
    theme_ipsum() +
    ylab("Lymphocyte (%)") +
    transition_reveal(RE_DATE)

#anim_save("lymphocyte_seq.gif")

albumin_seq <- patients_tests %>% filter(!is.na(albumin)) 

albumin_seq %>% mutate(PATIENT_ID=as.factor(PATIENT_ID)) %>%
  ggplot( aes(x=RE_DATE, y=albumin, group=PATIENT_ID, color=PATIENT_ID)) +
    geom_line() +
    geom_point() +
  facet_grid(rows=vars(outcome)) +
    ggtitle("Albumin value during patient hospitalization") +
    theme_ipsum() +
    ylab("Albumin") +
    transition_reveal(RE_DATE)

#anim_save("albumin_seq.gif")

Prediction model

In order to build an appropriate model, there is a need for the data cleaning. There shouldn’t be any NA in the data set. Moreover, every considered patient should have all of considered biomarkers tested at least once. For building the model purpose, the last existing test of each biomarker for each patient was taken. As it was shown before, many biomarkers are unsuitable because of containing too many NA in theirs columns. Which means that they should be removed from the data set. Below is the list of remaining columns:

outcome

age

gender

hemoglobin

eosinophils(%)

Alkaline phosphatas

albumin

basophil(%)

Total bilirubin

Platelet count

monocytes(%)

neutrophils(%)

total protein

mean corpuscular volume

hematocrit

White blood cell count

mean corpuscular hemoglobin concentration

Urea

lymphocyte count

Red blood cell count

Eosinophil count

neutrophils count

Direct bilirubin

(%)lymphocyte

Total cholesterol

aspartate aminotransferase

Uric acid

HCO3-

Lactate dehydrogenase

monocytes count

globulin

γ-glutamyl transpeptidase

basophil count(#)

mean corpuscular hemoglobin

glutamic-pyruvic transaminase

eGFR

creatinine

With condition of existing at least one of listed above biomarkers tests, 19 patients should be ignored in building model process.

cleaned_df <- cleaned_df %>% select(-PATIENT_ID)
set.seed(23)
inTraining <- 
    createDataPartition(
        y = cleaned_df$outcome,
        p = .70,
        list = FALSE)


training <- cleaned_df[ inTraining,]
testing  <- cleaned_df[-inTraining,]

rfGrid <- expand.grid(mtry = 10:30)
ctrl <- trainControl(
    method = "repeatedcv",
    classProbs = TRUE,
    number = 2,
    repeats = 5)

set.seed(23)
fit <- train(outcome ~ .,
             data = training,
             method = "rf",
             metric = "ROC",
             preProc = c("center", "scale"),
             trControl = ctrl,
             tuneGrid = rfGrid,
             ntree = 30)
rfClasses <- predict(fit, newdata = testing)
confusionMatrix(data = rfClasses, testing$outcome)

## Confusion Matrix and Statistics
## 
##           Reference
## Prediction Death Survival
##   Death       48        1
##   Survival     0       56
##                                           
##                Accuracy : 0.9905          
##                  95% CI : (0.9481, 0.9998)
##     No Information Rate : 0.5429          
##     P-Value [Acc > NIR] : <2e-16          
##                                           
##                   Kappa : 0.9808          
##                                           
##  Mcnemar's Test P-Value : 1               
##                                           
##             Sensitivity : 1.0000          
##             Specificity : 0.9825          
##          Pos Pred Value : 0.9796          
##          Neg Pred Value : 1.0000          
##              Prevalence : 0.4571          
##          Detection Rate : 0.4571          
##    Detection Prevalence : 0.4667          
##       Balanced Accuracy : 0.9912          
##                                           
##        'Positive' Class : Death           
##

Imporance of attributes

varImp(fit)

## rf variable importance
## 
##   only 20 most important variables shown (out of 36)
## 
##                              Overall
## `Lactate dehydrogenase`      100.000
## `(%)lymphocyte`               56.749
## `neutrophils(%)`              31.971
## `Eosinophil count`            17.594
## albumin                       15.974
## `neutrophils count`           14.715
## `monocytes(%)`                14.162
## `Platelet count`              13.067
## `lymphocyte count`            10.040
## `aspartate aminotransferase`   9.925
## `eosinophils(%)`               6.399
## age                            3.518
## `Direct bilirubin`             3.514
## `Total cholesterol`            3.081
## `HCO3-`                        2.540
## Urea                           2.374
## eGFR                           2.171
## `γ-glutamyl transpeptidase`    1.967
## `Alkaline phosphatase`         1.607
## `total protein`                1.047